| Literature DB >> 34525113 |
Allan Gottschalk1,2, Susanna Scafidi1, Thomas J K Toung1.
Abstract
Rats are frequently used for studying water content of normal and injured brain, as well as changes in response to various osmotherapeutic regimens. Magnetic resonance imaging in humans has shown that brain water content declines with age as a result of progressive myelination and other processes. The purpose of this study was to quantify changes in brain water content during rat development and aging. Brain water content was measured by standard techniques in 129 normal male Sprague-Dawley rats that ranged in age (weight) from 13 to 149 days (18 to 759 g). Overall, the results demonstrated a decrease in water content from 85.59% to 76.56% with increasing age (weight). Nonlinear allometric functions relating brain water to age and weight were determined. These findings provide age-related context for prior rat studies of brain water, emphasize the importance of using similarly aged controls in studies of brain water, and indicate that age-related changes in brain water content are not specific to humans.Entities:
Mesh:
Year: 2021 PMID: 34525113 PMCID: PMC8443050 DOI: 10.1371/journal.pone.0249384
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Curve fitting revealed a nonlinear relationship between brain water content and weight (A), a linear relationship between weight and age (B), and a nonlinear relationship between brain water content and age (C) for all 129 rats from the study.
The corresponding regression equations are given in each panel, where the functional forms of the nonlinear relationships in A and C are the same but with different parameters. A quadratic term for the linear regression in panel B was not contributory (p = 0.30). All parameters of the regression equations in each panel are significantly different from zero (p < 0.001) and are given along with their standard errors and quality of fit (R2) in Table 1.
Details of parameters for regression equations displayed in panels of Fig 1.
All parameters are significantly different from zero (p < 0.001).
| Regression equation | Parameter 1 | Parameter 2 | Parameter 3 | R2 |
|---|---|---|---|---|
| mean (SE) | mean (SE) | mean (SE) | ||
| 76.18 (0.26) | 54.19 (1.18) | 0.5800 (0.0431) | >0.99 | |
| -65.4 (4.2) | 5.52 (0.05) | NA | 0.99 | |
| 76.84 (0.16) | 166.42 (1.61) | 1.155 (0.066) | >0.99 |
NA = not applicable; SE, standard error of the mean.